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All Journal Jurnal Ilmu Komputer dan Informasi Jurnal Buana Informatika Teknosains: Media Informasi Sains dan Teknologi Jurnal Teknologi Informasi dan Ilmu Komputer SIGMA: Jurnal Pendidikan Matematika AlphaMath: Journal of Mathematics Education JOIV : International Journal on Informatics Visualization Al Ishlah Jurnal Pendidikan Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JPM (Jurnal Pemberdayaan Masyarakat) Faktor Exacta Jurnal Penjaminan Mutu JITK (Jurnal Ilmu Pengetahuan dan Komputer) JTAM (Jurnal Teori dan Aplikasi Matematika) CARADDE: Jurnal Pengabdian Kepada Masyarakat JURNAL PENDIDIKAN TAMBUSAI Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JURNAL MathEdu (Mathematic Education Journal) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) GERVASI: Jurnal Pengabdian kepada Masyarakat TELKA - Telekomunikasi, Elektronika, Komputasi dan Kontrol Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi Jurnal Sistem Informasi dan Informatika (SIMIKA) Reswara: Jurnal Pengabdian Kepada Masyarakat Jurnal Teknik Informatika (JUTIF) Unri Conference Series: Community Engagement Jurnal Dedikasi International Journal of Electronics and Communications Systems Jurnal Pengabdian Inovasi dan Teknologi Kepada Masyarakat Online Learning in Educational Research Seminar Nasional Pengabdian Kepada Masyarakat Catimore: Jurnal Pengabdian Kepada Masyarakat Jurnal Ilmiah Edutic : Pendidikan dan Informatika Internet of Things and Artificial Intelligence Journal Jurnal Penjaminan Mutu Indonesian Journal of Fundamental Sciences IPTEK: Jurnal Hasil Pengabdian kepada Masyarakat Teknovokasi : Jurnal Pengabdian Masyarakat Vokatek : Jurnal Pengabdian Masyarakat Information Technology Education Journal Journal of Embedded Systems, Security and Intelligent Systems Ininnawa: Jurnal Pengabdian Masyarakat Jurnal Kemitraan Responsif untuk Aksi Inovatif dan Pengabdian Masyarakat Jurnal Ilmu Pengetahuan dan Teknologi Bagi Masyarakat Jurnal MediaTIK Mekongga: Jurnal Pengabdian Masyarakat Media Elektrik Malaqbiq : Jurnal Pengabdian kepada Masyarakat. Sasambo: Jurnal Abdimas (Journal of Community Service) JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Journal of Emerging Research in Computer Science and Artificial Intelligence Pengabdian: Jurnal Abdimas
Andi Baso Kaswar Andi Baso Kaswar
Universitas Negeri Makassar
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Other

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CLASSIFICATION OF PAPAYA NUTRITION BASED ON MATURITY WITH DIGITAL IMAGE AND ARTIFICIAL NEURAL NETWORK Andi Ahmad Taufiq; Hanum Zalsabilah Idham; Muh Fuad Zahran Firman; Andi Baso Kaswar; Dyah Darma Andayani; Muhammad Fajar B; Abdul Muis Mappalotteng; Andi Tenriola
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.7070

Abstract

Papaya is a tropical fruit with high nutritional content and significant health benefits. Nutritional components such as sugars, vitamin C, and fibre are strongly influenced by ripeness level. Identifying these nutrients usually requires laboratory tests that are time-consuming and rely on sophisticated equipment. Previous studies have focused on classifying ripeness levels, yet none have specifically addressed the classification of nutritional content. This study proposes a classification system for papaya nutrition based on ripeness using digital image processing and artificial neural networks (ANN). The method consists of six stages: image acquisition, preprocessing, segmentation, morphology, feature extraction, and classification with a trained ANN model. Experiments were conducted to evaluate feature combinations, including colour and texture features. The combination of LAB colour features and texture features-contrast, correlation, energy, and homogeneity-produced the best results. Testing on 75 images achieved an average precision of 97.22%, recall of 96.67%, F1-Score of 96.80%, and accuracy of 97.33%, with an average computation time of 0.02 seconds per image. These findings indicate that the proposed method provides fast and highly accurate classification of papaya’s nutritional content, offering a practical alternative to laboratory testing. Nevertheless, the study is limited by the relatively small dataset and controlled acquisition environment. Future research should extend the dataset, incorporate deep learning approaches, and validate performance under real-world conditions to enhance robustness and generalization
Lightweight Image-Based Mold Detection System for Real-Time Bread Quality Monitoring Using Artificial Neural Networks (ANN) Saputra, Nikola; Ilyas, Muh.; Riswansyah , Muh Fikra Junian; Kaswar, Andi Baso; Lamada, Mustari S.
International Journal of Electronics and Communications Systems Vol. 5 No. 2 (2025): International Journal of Electronics and Communications System
Publisher : Universitas Islam Negeri Raden Intan Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ijecs.v5i2.28706

Abstract

Mold contamination of white bread is an ongoing challenge for quality monitoring, while conventional visual inspection remains unreliable for early and consistent detection. This study aims to propose a lightweight image-based mold detection system for white bread oriented towards real-time quality monitoring using Artificial Neural Networks (ANNs). An experimental workflow combining digital image acquisition, pre-processing, Otsu-based segmentation, morphological refinement, multicolor color space feature extraction, and an Artificial Neural Network (ANN) classifier is implemented. Results indicate that color information is the dominant discriminatory cue for mold identification, while texture descriptors provide complementary structural information that improves class separation. The RGB+HSV+LAB combination achieved the highest performance, with a training accuracy of 97.91 percent and a testing accuracy of 96.66 percent. These findings demonstrate that effective mold classification can be achieved without relying on deep or computationally intensive architectures when the feature representation is well-designed. In conclusion, a lightweight, feature-centric ANN (Artificial Neural Network) is sufficient for reliable classification of mold growth levels on white bread. This study confirms that a compact, feature-based learning strategy is sufficient for reliable classification of mold on white bread, providing a technically efficient basis for a vision-based food quality assessment system.
KLASIFIKASI TINGKAT KESEGARAN DAUN BAWANG MENGGUNAKAN JARINGAN SYARAF TIRUAN BERBASIS PENGOLAHAN CITRA DIGITAL Andi Fitri Novianti; Muhammad Atthariq; Juliano Nufiansyach Dini; Andi Baso Kaswar; Jessica Crisfin Lapendy
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 7 No. 2 (2024): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v7i2.3378

Abstract

Green onions, commonly used in Indonesian cuisine, have significant agricultural potential. Despite high production, their quality, particularly freshness, is traditionally evaluated visually, leading to inconsistent and subjective results. This study aims to develop an objective and accurate method for classifying the freshness of green onions using an Artificial Neural Network (ANN). Previous studies have employed ANN but have not specifically targeted the freshness classification of leeks. The proposed method utilizes the color and texture features of green onions.The research methodology includes image acquisition, preprocessing, segmentation, morphology, feature extraction, and classification using ANN. A total of 300 images were acquired and categorized into three freshness levels: not fresh, less fresh, and fresh. During the training phase, 240 images were used, and 80 images were reserved for testing. The optimal feature combination identified includes HSV and LAB color features along with texture features (Contrast + Energy). The results demonstrated that the freshness classification of green onions achieved 100% accuracy in both training and testing phases. The training process, with 240 images, had a computation time of 142.684 seconds, while the testing process, with 80 images, took 35.648 seconds. These findings indicate that using ANN based on color and texture features is highly effective in determining the freshness level of green onions.
Pengembangan Sistem Informasi Pariwisata Kabupaten Soppeng sebagai Media Informasi Berbasis Web Aisyah Ramadani; Dyah Darma Andayani; Andi Baso Kaswar
Journal of Emerging Research in Computer Science and Artificial Intelligence Vol 1, No 2 (2026): Maret 2026
Publisher : PT. Global Research Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Tujuan penelitian ini yakni untuk menghasilkan sistem informasi pariwisata kabupaten Soppeng sebagai media informasi berbasis web agar objek wisata di kabupaten Soppeng dikenal banyak orang dan mengetahui hasil pengujian sistem menggunakan ISO 25010 dengan delapan aspek pengujian. Penelitian ini menggunakan model pengembangan prototyping dengan tahapan: pengumpulan kebutuhan sistem, membangun prototyping, evaluasi prototyping, mengkodekan sistem, menguji sistem, evaluasi sistem, menggunakan sistem. Adapun hasil yang didapatkan dari penelitian ini yaitu: (1) Aspek suitability diperoleh hasil presentase 100% dengan kategori “Layak”. (2) Aspek reliability diperoleh hasil presentase 100%. (3) Aspek usability diperoleh hasil presentase 90% dengan kategori “Sangat Layak”. (4) Aspek efficiency diperoleh hasil presentase 86% dengan kategori grade B. (5) Aspek maintainability diperoleh hasil memenuhi standar dari indiktor instrumentation, consistensy, dan simplicity. (6) Aspek portability diperoleh hasil sistem dapat berjalan dengan baik diberbagai browser baik dekstop maupun mobile. (7) Aspek security diperoleh hasil dengan kategori A. (8) Aspek compatibility diperoleh hasil sistem yang dibangun kompatibel dengan berbagai browser. Berdasarkan hasil pengujian tersebut sistem informasi pariwisata kabupaten Soppeng sebagai media informasi berbasis web telah memenuhi standar kualitas sistem dan sangat layak digunakan.
Pengembangan Sistem Informasi Kepegawaian SMA Negeri 2 Polewali Berbasis Web Sanatang; Andi Baso Kaswar; Jamila
Information Technology Education Journal Volume 1, Issue 3, September 2022
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1138.746 KB) | DOI: 10.59562/intec.v1i3.249

Abstract

Tujuan daripada penelitian ini adalah untuk mengetahui hasil pengujian sistem informasi kepegawaian SMA Negeri 2 Polewali berbasis web dan keefektifan dan kepraktisan sistem informasi kepegawaian SMA Negeri 2 Polewali berbasis web. Penelitian ini merupakan jenis penelitian dan pengembangan Research and Development (R&D) yang menggunakan model pengembangan prototype dengan menggunakan pengujian standar ISO 25010. Hasil pengujian menunjukkan hasil yang sangat baik berdasarkan 8 aspek karakteristik ISO 25010 yakni: functional suitability dengan hasil 100% dengan kategori dapat diterima, usability dengan hasil pengujian 30 responden sebanyak 22 orang atau 73,3% pada kategori sangat baik dan 8 orang atau 26,7% pada kategori baik, performance efficiency hasil page speed sebesar 94% dengan waktu load 1,3 detik, reliability dengan hasil 100%, Portability dengan hasil dapat berjalan dengan baik di berbagai macam sistem operasi dan browser, maintainability telah memenuhi aspek yang ditentukan yaitu instrumentation, consistency, dan simplicity, compatibility dengan hasil 100% dengan kategori sangat layak, security berada pada tingkat keamanan level C low.
Pengembangan E-Modul Mata Kuliah Sistem Operasi di Program Studi PTIK Universitas Negeri Makassar Ikra Ain Fahwa; Riana T. Mangesa; Andi Baso Kaswar
Information Technology Education Journal Volume 2, Issue 2, Mei 2023
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (471.82 KB) | DOI: 10.59562/intec.v2i2.270

Abstract

Penelitian ini bertujuan untuk mengetahui validitas, kepraktisan, dan keefektifan pengembangan e-modul mata kuliah Sistem Operasi di Jurusan Teknik Informatika dan Komputer Fakultas Teknik Universitas Negeri Makassar. Penelitian ini merupakan penelitian pengembangan (Research and Development), rancangan pengembangannya menggunakan model 4-D. Subjek pada penelitian ini adalah mahasiswa Program Studi PTIK Universitas Negeri Makassar. Instrumen pengumpulan data dilakukan melalui lembar uji validasi, angket respon mahasiswa, dan instrumen penilaian hasil belajar. Teknik analisis data yang digunakan adalah analisis data deskriptif. Dari penelitian ini, hasil yang diperoleh menunjukkan bahwa e-modul Sistem Operasi di Program Studi PTIK Universitas Negeri Makassar dinyatakan valid berdasarakan hasil validasi oleh ahli materi dan media yang berada pada kategori sangat valid. E-modul Sistem Operasi dinyatakan praktis berdasarkan frekuensi tanggapan responden pada uji coba kelompok kecil dan uji coba kelompok besar dengan kategori sangat baik. E-modul Sistem Operasi dinyatakan efektif karena memberikan kemudahan bagi mahasiswa dalam proses pembelajaran sehingga efektif meningkatkan hasil belajar, ditinjau dari hasil belajar mahasiswa dengan kategori lulus dengan nilai tinggi sehingga dinyatakan efektif. Berdasarkan data tersebut dapat disimpulkan bahwa e-modul Sistem Operasi valid, praktis, dan efektif untuk digunakan sebagai bahan ajar bagi mahasiswa Program Studi PTIK Universitas Negeri Makassar.
Sistem Klasifikasi Tingkat Kematangan Buah Cabai Katokkon Berdasarkan Fitur Warna LAB Menggunakan Artificial Neural Network Backpropagation Andi Baso Kaswar; Fhatiah Adiba; Dyah Darma Andayani
Journal of Embedded Systems, Security and Intelligent Systems Vol 4, No 2 (2023): November 2023
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v4i2.996

Abstract

Chili is one of the horticultural commodities that has a very significant economic and cultural value in Indonesia. One type of chili that is unique but widely cultivated in Indonesia is katokkon chili (Toraja chili). Seeing the great potential that katokkon chili has, the chili is finally widely cultivated. However, various problems in the cultivation process until harvest have emerged. One of them is in the process of identifying the level of maturity. The stage of identifying the maturity level of chilies is an important aspect of cultivation and post-harvest handling. This is because the maturity level significantly affects the quality, nutritional content, and market value of chili katokkon. Many studies have utilized digital image processing and machine learning in fruit ripeness detection. However, until now, the detection of the maturity level of katokkon chili fruit is still done manually, which has an impact on the potential inaccuracy of classification results due to various factors. Therefore, this research proposes a classification system for the maturity level of katokkon chili fruit based on LAB color features using artificial neural network backpropagation. The proposed method consists of six main stages, namely image acquisition, preprocessing, segmentation, morphological operations, LAB feature extraction, and backpropagation artificial neural network modeling. The proposed method can classify the maturity level of chili katokkon into three classes with 96,00% accuracy, 96,40 % precision, and 96,00% recall. These results show that the proposed method can classify the maturity level of chili katokkon accurately.
Utilizing the K-Means Clustering Algorithm for Analyzing Student Achievement Assessment at SMK Negeri 1 Gowa Andi Akram Nur Risal; Dyah Darma Andayani; Muh Ilham Suherman; Andi Baso Kaswar
Journal of Embedded Systems, Security and Intelligent Systems Vol 5, No 1 (2024): March 2024
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v5i1.2178

Abstract

Student achievement assessment is an integral part of the educational process that aims to measure student learning achievement. This study aims to analyze student achievement assessments at SMK Negeri 1 Gowa using the K-Means algorithm. This study uses student data from the 2021–2022 school year, grouped into three clusters: highest, medium, and sufficient. The analysis results show that K-Means successfully clusters students based on academic achievement. The first cluster displays focused students who excel in a few key subjects (PPKN, Physics, Chemistry, and Math); the second cluster shows students with excellence in certain subjects (PAI, Bahasa Indonesia, and History); and the third cluster displays students with the highest academic achievement in all subjects. Evaluation using the silhouette coefficient shows that cluster one has a range of 0.49–0.54, cluster two has a range of 0.49–0.56, and cluster three has a value of 0.50–0.55, indicating that the data density in each cluster is good. SMK Negeri 1 Gowa can use the results of this study as a basis for school evaluation to enhance student achievement.
INTEGRATION OF OTSU THRESHOLDING AND MORPHOLOGICAL OPERATIONS FOR CAROLINA REAPER CHILI IMAGE SEGMENTATION Andi Baso Kaswar; Labusab; Ismail Aqsha
Journal of Embedded Systems, Security and Intelligent Systems Vol 5, No 3 (2024): November 2024
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Segmentation is an important step in building a fruit-quality classification system. Previous research has shown the success of the Otsu Thresholding method for fruit image segmentation, but its application to Carolina Reaper chili images in Indonesia has not been carried out specifically. This research proposes the integration of the Otsu Thresholding method with morphological operations to improve the accuracy of Carolina Reaper chili image segmentation based on the ripeness level. The process starts with RGB image acquisition using a controlled camera, followed by red channel extraction as the segmentation input. The Otsu method is used to separate the object and background based on pixel intensity, resulting in a binary image that is enhanced through morphological operations, including dilation, imfill, and bwareaopen. The results show high accuracy, with averages of 99.85% (mature), 99.38% (almost mature), and 99.67% (raw). The average computation time is less than one second which shows the potential for real-time applications. This research contributes to the efficiency of technology-based postharvest processing of Carolina chili peppers
Accuracy and Detection Efficiency in Stacked PVC-based Transparent Markers for Augmented Reality Systems Kurnia Prima Putra; Andi Baso Kaswar; Muh. Ihsan Zulfikar; Tri Afirianto
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 3 (2025): September 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i3.10447

Abstract

Augmented Reality (AR) marker-based tracking has evolved from single-marker systems, which limit spatial coverage and robustness, to multi-marker approaches that enable simultaneous detection for expanded interactivity. Prior research using libraries like Vuforia and AR.js has applied multi-markers in education (e.g., solar system simulations) and interactive media, improving redundancy and workspace; however, stacked transparent overlays—treating layers as unified yet distinct entities—remain underexplored, especially for alignment precision and opacity effects in translucent materials like PVC.This study evaluates tracking accuracy and detection efficiency of stacked transparent markers (2–7 layers) in AR.js, printed on 0.3 mm PVC sheets and tested with a Vivo V30 Pro camera under controlled lighting. Well-aligned configurations maintain accuracy near single-marker baselines (0.85 mm to 1.15 mm deviation), with detection rates from 98.5% to 88.3%, though processing time rises from 16.9 ms to 25.9 ms, lowering frame rates from 59.2 FPS to 38.6 FPS. Translational (2–5 mm) and rotational (5°–15°) misalignments sharply degrade performance, reducing 7-layer detection to 65.4% and 58.2%, respectively.The primary contribution is quantitative benchmarks for PVC-based overlays in AR.js, confirming feasibility for multi-layered tracking while defining tolerances (<2 mm translation, <3° rotation) to sustain >90% detection in 3–5 layers. These insights guide AR design for robust, extended applications in education and industry.
Co-Authors A Mutahharah A. Farha Adella A. Muhammad Idkhan A. Mutahharah A. Mutahharah Mutahharah A.Farha Adella Abd. Rahman Patta Abdul Muis Mappalotteng Abdul Wahid Adiba, Fhatiah Afdhaliyah, Mukhlishah Aglaia, Alifya Nuraisyar Agung, Andi Sadri Agus Zainal Arifin Agus Zainal Arifin Agustinus Suria Darme Ahmad Adzan Lain Ahmad Fudhail  Majid Ahmad Khan, Sardar Faroq Ahmad Mustofa Hadi Ahmad Mustofa Hadi Ainun Zahra Adistia Aisyah Ramadani Akbar, Trisakti Akil, Muhammad Aksa, Muhammad Al Imran Alfian Firlansyah Ananta Dwi Prayoga Alwy Andi Ahmad Taufiq Andi Akram Nur Risal Andi Alamsyah Rivai Andi Fitri Novianti Andi Nurul Izzah Andi Rosman N Andi Tenri Ola Rivai Andi Tenriola Anggy Heriyanti Anggy Heriyanti Annajmi Rauf Anny Yuniarti Aprilianti Nirmala S Aqsha, Ismail Aras, Muh Riski Farukhi Arifky, Reza Arinanda Alviansyah Arini Ulfa Mawaddah Arliandy, Arliandy Arya Yudhi Wijaya Arya Yudhi Wijaya Aryadi Nurfalaq Ashadi, Ninik Rahayu Asmi Ulfiah Asnidar Asnidar Asrofi, Muhammad Ghufran Aswar Aswar Aulia, Magfirah Awalia, Nur Ayu Futri Ayu Safitri Azis, Putri Alysia Azis, Salsabila Bantun, Suharsono Bugdady, Andi Jaedil Bukhari Naufal Nur A.G Burhan, Rafli Ananta Chairati, Chairati Cyahrani Wulan Purnama Cyahrani Wulan Purnama Rasyid Darma Andayani, Dyah Darme, Agustinus Suria Della Fadhilatunisa Desitha Cahya Dewi Fatmarani Surianto Dhanendra, Fadhil Dina Salam, Fitria Nur Dirawan, Gufran Darma Edy, Marwan Ramdhany Elva Amalia Elva Amalia Eman Wahyudi Kasim Eriyani, Nindy Sri Fachriansyah, Zaky Farid, Muhammad Miftah Farros Taufiqurrahman Fathahillah Fathahillah Fauzi, A. Arfan Fazli Arif Fhatiah Adiba Fhatiah Adiba Hafidz Muhtar Hanum Zalsabilah Idham Hartanto Tantriawan Heriyanti, Anggy Herman Hermansyah Hermansyah Hersyam, Muh Syachrul Hidayat, Muh. Taufik Ibnu Fikrie Syahputra Idkhan, A. Muhammad Idkhan, Andi Muhammad Idris, Muh Gimnastiar Ihlasul Amal Ikra Ain Fahwa Ilham, Muh Ilham, Muhammad Ryan Ilyas, Muh. Indri Pratiwi Ramadhani Intam, Reski Nurul Jariah S Irwansyah Suwahyu Ishak Israwati Hamsar Iwan Suhardi Jamaluddin, Bunga Mawar Jamila Jamila Jamila Jariah S.Intam, Rezki Nurul Jasruddin Daud Malago Jayanti Yusmah Sari Jessica Crisfin Lapendy Juliano Nufiansyach Dini Jumadi Mabe Parenreng Jusrawati Jusrawati Jusrawati Kaparang, Adam Indra Kaswar, A Baso Kurnia Prima Putra Kurnia Wahyu Prima Labusab Labusab Labusab Labusab, Labusab Lapendy, Jessica Crisfin M. Miftach Fakhri Makmur, Haerunnisya Mappaita, Al Haytsam Marwan Eka Ramdhany Marwan Ramdhany Edy Massie, Gary Jeremi Maulana Muhammad Mawaddah, Arini Ulfa Meisaraswaty Arsyad Muammar Muammar Muh Aldhy Fatahillah Muh Devan Fahresi Muh Fuad Zahran Firman Muh Ilham Suherman Muh Omar Hassan ST Muh. Dirgafa Anugra Rais Muh. Dirgafa Anugrah Rais Muh. Fardika Pratama Putra Muh. Fauzan Arifuddin Muh. Ihsan Zulfikar Muh. Rais Muh. Rasul D Muhammad Agung Muhammad Agung Muhammad Akbar Muhammad Akbar Muhammad Atthariq Muhammad Fajar B Muhammad Naim Muhammad Nur Yusri Maulidin Yusuf Muhammad Nur Yusri Maulidin Yusuf Muhammad Yahya Muhiddin Palennari Muhira Muhira Muhtar, Hafidz Mukhtar Mukhtar Mulia, Musda Rida Muliaty Yantahin Musdar, Devi Miftahul Jannah Musgamy, Muh Faqih S Mustari Lamada Naim, Muhammad Nasrullah, Asmaul Husnah NFH, Alifya Ninik Astuti Nirsal Nur Anny S. Taufieq Nur Fadillah Bustamin Nur Inayah Yusuf Nurbaitul Afyan Nurfalaq, Aryadi Nurfitri, Andi Aisyah Nurhidayat Nurhidayat Nurhikma Nurhikma Nurhikma Nurjannah Nurjannah Nurjannah Nurjannah Nurjannah Nurjannah Nurjannah Nurul Amanda Pratiwi Hasbullah Nurul Isra Humaira B Nurul Istiqamah Qalbi Nurul Izzah Dwi Nurul Izzah Dwi Nurdinah Nurwijayanti Patongai, Dian Dwi Putri Ulan Sari Perdana, Am Akbar Mabrur Pramudya Asoka Syukur Pratama, Azir Zuldani Putri Nirmala Putri Ramdani R, Muh Raflyawan R, Ranir Aftar Radha Hasda Halfis Ranggareksa, Andi Ranir Atfar R Rapa, Wiwi Resky, Andi Aulia Cahyana Riana T. Mangesa Ridwan Daud Mahande Ridwansyah Ridwansyah Risaldi, Muhammad Riswansyah , Muh Fikra Junian Riyama Ambarwati Rosidah Rosidah Rosidah Rusli, Risvan S, Mushawwir Sahribulan Sahribulan Saiful Bahri Musa Sakira, Tiara Putri Sam, Muh Hadal Ali Sanatang Saparuddin Saparuddin Saprina Mamase Saputra, Nikola Sartika Sari Sartika Sari Sasmita Sasmita Sasmita SATRIYAS ILYAS Silvia Andriani Soeharto Soeharto SR, Amin Farid Dirgantara Sri Rahayu St. Fatmah Hiola Suharsono Bantun Suhartono, Suhartono Supria Supria Surianto, Dewi Fatmawati Susiana Sari Syamsuddin Syasikirani. N, Adelia Tenriajeng, Andi Afrah Tenriola, Andi Tri Afirianto Tsabita Syalza Billa Tsabita Syalza Billa Irawan Umar, Nur Fadhilah Wahda Arfiana AR WAHYUDI Wanda Hamidah Wardani, Ayu Tri Wiwi Rapa WULANDARI Yasser Abd Djawad Yuliarni, Tarisa Yusuf, Zulfatni Zsolt Lavicza